Pharmacy News

Entries for March 2019

An Update on Controlled Substances Rules

 

Bryan Liptak, Pharm.D. 
PGY-1 Resident;  
Timothy Ekola, Pharm.D., MBA Director of Pharmacy 
Sparrow Health System / Comprehensive Pharmacy Services  

On Jan. 4, 2019, the Michigan Department of Licensing and Regulatory Affairs (LARA) Board of Pharmacy updated their controlled substance rules. One important rule change to take note of is that opioid and other controlled substances awareness training is required for all controlled substance licensees who prescribe or dispense controlled substances.  Controlled Substance Rule 338.3135 also prohibits the delegation of other licensees, unless the delegated licensee complies with the training requirement. It specifically states that advance practice registered nurses, registered professional nurses and licensed practical nurses must comply with the rule to be delegated to prescribe, dispense or administer a controlled substance.

The opioid and controlled substances awareness one-time training must cover all of the following standards:

  1. Use of opioids and other controlled substances
  2. Integration of treatments
  3. Alternative treatments for patient management
  4. Counseling patients on effects and opioid-associated risk
  5. Stigma of addiction
  6. Utilizing Michigan Automated Prescription System (MAPS)
  7. State and federal laws pertaining the prescribing and dispensing controlled substances
  8. Security and disposal requirements of prescriptions
Note that the standards may be obtained from more than one program. The department may select and audit licensees and request documentation of proof of completion of training. Controlled Substance Rule 338.3135 takes effect for initial licenses issued after Sept. 1, 2019, and with the first renewal cycle for controlled license renewals.

The complete details to this rule change, as well as all other rule changes to the Controlled Substances rule set, can be found at www.Michigan.gov/LARA or www.Michigan.gov/BPL.
Posted in: Member News
NHSN Antimicrobial Use and Resistance Modules - A Worthwhile Endeavor?
Derek VanderHorst, Pharm.D., BCPS, BCIDP
Clinical Pharmacy Specialist - Adult Infectious Diseases
Spectrum Health
 

On Jan. 1, 2017, The Joint Commission's standards on antimicrobial stewardship for inpatient acute care settings went into effect. Since that time, many institutions have implemented and/or expanded their antimicrobial stewardship efforts. No matter if your antimicrobial stewardship program (ASP) is in its infancy or has existed for years, they all share the same need for high quality data to identify areas for improvement or highlight the impact of the program. The Centers for Disease Control and Prevention (CDC) recommends a variety of metrics that an ASP may use; including antimicrobial purchasing costs, defined daily doses (DDD) and days of therapy (DOT). Quite often the primary metric used by an ASP is selected based on the available resources for your institution; the CDC recommends that if possible, DOT is used as the aforementioned primary metric.1 

In addition to metrics that measure antimicrobial utilization, ASPs often include their infection prevention department for assistance with tracking antimicrobial resistance rates over time and healthcare acquired infection rates. Many of the healthcare acquired infections, like healthcare, onset Clostridioides difficile rates, are collected and reported to the National Healthcare Safety Network (NHSN).

In an effort to compile these antimicrobial use and resistance data from across the country, NHSN created the Antimicrobial Use and Resistance (AUR) module for voluntary reporting of DOT, as well as, trends in antimicrobial resistance.2 In Nov. 2018, Spectrum Health Grand Rapids began reporting data to the Antimicrobial Use module of NHSN and shortly after in May 2018 all Spectrum Health hospitals also began contributing data to the NHSN AUR module. With this advancement, our ASP was given the opportunity to significantly improve the quality of data for which we used to measure our program. The NHSN AUR module has a plethora of pre-made reports that can aid stewardship personnel in identifying outlying antimicrobials, measuring trends in usage, and simply determining the most commonly utilized antimicrobials in your institution.2 

In addition to these more global, high-level reports, the NHSN AUR Module can quickly provide detailed information on the specific number of calendar days an antimicrobial was administered to a patient for the entire hospital or a particular nursing unit in question. The AUR module can even take these data one step further and report the specific route that a medication was given by providing the data in one of four categories: intravenous, respiratory, digestive and respiratory. All of the above data can then be automatically standardized per 1,000 patient days present so that month to month (or year to year) comparisons can be made. Spectrum Health was able to utilize this data to compare antimicrobial use between similar intensive care units, as well as, identifying areas in our system that were significant outliers in the use of carbapenems.

Another pre-built report offered by the AUR module is the Standardized Antimicrobial Administration Ratio (SAAR) that can be used to measure the antimicrobial utilization of one facility to another. It is similar in concept to the Standardized Infection Ratio (SIR) that is commonly used by our Infection Control departments. While caution should be used when blindly comparing the SAARs of two different institutions, we found it beneficial to compare two similar units within our facility, as well as, identifying outlying areas in our system that had either an excessively high or low SAAR. In addition to the benefit that the SAAR can have for your institution, participating in the AUR module allows you to contribute data to SAAR database that will only strengthen this metric for others as the years go by.

The use of the NHSN AUR module has been our quality metrics significantly more meaningful to not only the ASP personnel, but to our leaders as a whole. The module allows for the creation of digestible graphics that can easily illustrate trends over time and graphs to visually depict percent changes in antimicrobial utilization. Those of us that work in antimicrobial stewardship know that it can be difficult to convey all the complexities that impact our bottom line metrics. The data obtained from the NHSN AUR module has made it much easier for our ASP personnel to tell the story of antimicrobial stewardship within our system. While preparing your institution to report data to the NHSN AUR module can be complex, the return on investment can be well worth it. For specific details on how to begin the process of reporting to NHSN's AUR module, look for Dr. Rand Sulaiman's article in the May 2019 edition of the MSHP Monitor.

References:

  1. Centers for Disease Control and Prevention [Internet]. Atlanta (GA): CDC; c1946-2017. Core Elements of Hospital Antibiotic Stewardship Programs; 2016 May 25 [cited 2017 Jan. 17]; [about 15 screens]. Available from: https://www.cdc.gov/antibiotic-use/healthcare/implementation/core-elements.html

  2. Centers for Disease Control and Prevention [Internet]. Atlanta (GA): CDC; c1946-2018. Antimicrobial Use and Resistance Module; 2019 Jan. [cited 2019 Jan. 28]; [about 53 screens]. Available from: https://www.cdc.gov/nhsn/pdfs/pscmanual/11pscaurcurrent.pdf


Posted in: Member News
The Importance of Analytics in Pharmacy Practice

Nadia Haque, Pharm.D., MHSA, BCPS, FASHP

Director, Precision Medicine Program
Henry Ford Health System

The increasing role of healthcare analytics has immense opportunity and potential for the pharmacy world in ways that are growing "bigger" than ever before. The new era of big data, combined with healthcare analytics, can help organizations optimize medication use, improve safety and outcomes and reduce costs.

Big Data is defined as data sets that are so large and complex, that traditional computing methods are not capable of handling the information. Sources of Big Data in healthcare are the electronic medical record (EMR), administrative claims information, and clinical decision support databases, like First Databank or Theradoc. EMRs include real time clinical data such as patient identifiers, vital signs, diagnoses, medication administration and cost data, transition of care information and billing data (i.e. visit encounters coded with CPT codes).1

Given the rising cost of pharmaceuticals, as well as the need for health system pharmacy leaders to continuously demonstrate value, the aforementioned information can be extremely useful from both a clinical and operational perspective - beyond the traditional dashboard and bench marking initiatives.

Big Data and analytics may be used for creation of real time predictive models to provide guidance on examples such as the following:

  1. Which patients are more likely to be readmitted; i.e. which patients a pharmacist should spend extra time on counseling or with medication reconciliation.

  2. Which patients have gaps in care from a population health perspective; i.e. patients that have not received specific vaccinations, have out of range HbA1Cs or are not adherent to their medication therapies.

  3. Drug diversion that may be occurring at an institution, as demonstrated by statistical variances in large amounts of administration data.

  4. Variations in provider utilization, which may demonstrate opportunities for clinical standardization and reduced drug costs.

  5. Drugs and clinical situations that are more likely to cause adverse events in patients.

  6. Patients more likely to acquire hospital associated infections.

  7. Optimal staffing ratios and potential different measurements on a pharmacist's impact on patient outcomes and reduction of costs.2

Pharmacists and pharmacy departments are uniquely positioned to be successful in this space, given their analytic skills, and advanced use of technology. Continuing to train pharmacists in informatics will be key, as will the knowledge of study design, interpretation and data analysis skills. Front line pharmacists will be instrumental in adoption of such analyses, as they possess knowledge of day-to-day clinical activities that are important to measure. Additionally, such data may need to be analyzed using machine learning, deep learning or other artificial intelligence techniques - which may require skillsets not traditionally possessed by pharmacists. 

Partnering with data science colleagues within an institution may provide a further level of analysis unable to be completed by traditional programming methodologies.

What unique things have you done to incorporate analytics at your institution? Please share ideas, or any other discussion points you may have surrounding pharmacy informatics at our Michigan Society of Health-System Pharmacists Google Group Listserve or email 

MSHP-Informatics@googlegroups.com. 

References:

  1. Stokes LB, Rogers JW, Hertig JB, Weber RJ. Big data: implications for health system pharmacy. Hosp Pharm 2016;51:599-603.

  2. The Growing Focus on Pharmacy Data Analytics. https://www.stlcop.edu/news/pharmacy-informatics.html. Accessed Jan. 15, 2019.



Posted in: Member News
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